Similarity measure of hesitant fuzzy sets based on symmetric cross entropy and its application in clustering analysis

The clustering method is studied under the hesitant fuzzy environment. Firstly, the hesitant fuzzy relative entropy and symmetric cross entropy are defined. From the viewpoint of information theory, a new similarity measure between two hesitant fuzzy sets is proposed. Then, a similarity coefficient matrix is constructed, and a netting method is presented to make clustering analysis for hesitant fuzzy sets. Finally, a numerical example is given to verify the effectiveness of the proposed method.